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RESEARCH METHODOLOGY
GK Mbassa
INTRODUCTION TO RESEARCH
Objective Introduces researchers and
students to scientific research methods, enable them prepare research proposals in:
Veterinary Sciences Animal health Animal production Biotechnology Medicine Biomedical and Laboratory
sciences Agriculture Wildlife Others
CONTENTS
1. Introduction; Research planning and process
2. Types of research 3. Problem identification
process 4. Literature review on subject
5. Factor-Outcome relationship 6. Measurements 7. Research designs 8. Data collection 9. Data processing, analysis,
and management
10. Data presentation 11. Research project
description 12. Report writing 13. Research ethics
Introduction
Research is a systematic for search or inquiry for information (new information)
Research purpose is to explore, describe, explain and control
Stages in Research
Planning stage Data collection (gathering
the information) Data analysis (processing
data to yield knowledge)
Interpreting the data (extracting the knowledge and information)
Results utilization phase
(a) Building the concept (b) Problem search (c) Research justification (d) General objectives
Planning stage
(e) Specific objectives (f) Assumptions (g) Limitations (h) Hypotheses themes, arguments
(i) Operational concepts
(j) Planning of research and purpose;
(k) Literature review(l) Proposal write up
Data collection (gathering the information)
(a) Population source of data
(b) Logistics of data collection
(c) Collection of samples from the population
Data analysis (processing data to yield knowledge)
Facilities for data analysis Laboratory procedures Treated samples Control samples Recording of results Statistical procedures
Interpreting the data (extracting the knowledge and information)
Data grouping and splicing Tables and figures Means and trends; Equivocal and unequivocal
conclusions
Results utilization Identify beneficiaries of
results (solved problem, generated technology)
Professional Research Report Scientific briefs Communications of
knowledge and technology
Seminars and workshops Policy changes Further research or
activities Publication of data Patent technology Apply/ Sell technology Sales of research products
and technology
Variables in Research
Variables are factors, parameters, attributes or qualities of the cases that are being measured or recorded, examples being sex, age, height, weight, colour, number etc and are either independent or dependent.
Variables vary in their scores on the different attributes, observations, records or population numbers.
Independent variables, also called predictor or explanatory variables are the factors that cause the variation in the dependent variable
Dependent variable is the outcome resulting from the independent variable
TYPES OF RESEARCH
Several categories On basis of numerical
principles1. Qualitative2. Quantitative3. Both qualitative and
quantitative
Qualitative; describes/analyzes culture and behaviour of humans, animals, plants, materials, cells, flowers, fruits, organic or inorganic matter, for example staining characteristics, organoleptic tests of foods such as taste, smell, colour, consistence, interactive, opinions, feelings. In social sciences qualitative research is called naturalistic inquiry or field studies.
Qualitative research Ethnographies (observations of
groups); Phenomenologies, studying
subjects over a period of time; Case studies to investigate subject
over time
Quantitative Research Uses numeric data to verify,
confirm, prove causation, correlation, corroboration or substantiation
Establishes cause-effect relationship, focusing on measurements, assigning of numerical events according to rules
Application of quantitative research
Requirement for statistical tests Quantify extent of cause of effect Frequencies needed to explain
meanings, collects numerical data to explain phenomena;
Discovery of unexpected and in-depth investigation
In finding effect, control of one or more factors required. Rigorous and rigid methodologies and all procedures are specified before data collection and consistently followed
Data analysis is statistical (deductive)
Scenario is artificial, as in a laboratory.
Qualitative and quantitative research complement each other
Combined in biological systems to maximize strengths and minimize limitations of each.
Other Research types criteria Basic sciences: —study of macro
and micro morphology & physiology
Applied sciences; —apply knowledge to develop drugs, vaccines, seeds, molecules, genes and more;
Observational (descriptive)- observe activities in natural or artificial systems and reports the findings by description
Analytical: — test causal association between factor and outcome
Evaluative studies:—find out whether some factor introduced in a population has imparted any influence (retrospective studies)
Innovative: —new inventions; technology, materials and testing alternative technologies
Experimental (research designed to test hypothesis)
Surveys; prevalence of diseases, distribution of a factor. A survey to screen and detect existence of a factor in a population, and determine (or not), the magnitude of the factor
RESEARCH PROBLEM IDENTIFICATION
Purpose of research; solve problem Identification is essential Develop ideas from your subject Purpose new knowledge solve
problem Know client of the research results
Characteristics of a good research question
Very clear; Specific; Elements, variables &
factors affecting variables definable and measurable;
Investigations on the question achievable within given time
Clear relationship between research question and hypothesis;
Research question fitted into hypothesis and statistical tests;
Research question methods and assumptions well definable
Source of data well definable; primary, secondary, routine or published data
Factors affecting research process defined; logistics and finances
Research on problem and question acceptable scientifically, socially, politically and budgetary
Research question has not been previously answered
A researchable problem is found through:
A directly observed problem or puzzle in science, social, economic, cultural, development, political or other systems, animals, plants, materials, foods, diseases, machines, education, health, weather and many others;
A thorough review of literature on the subject and on closely related subjects to find out what exists, what has already been done and what the exact problem is and how it can be solved in terms of experimental design, materials and methods
Improvement of existing systems, machines, life of organisms, foods (palatability, nutritive value, contents, effects), forests, domestic animals, wildlife, cells, drugs, vaccines and other biological products
To evaluate effectiveness of systems, materials, vaccines, drugs, shelf life of goods, animal/human nutrition, knowledge delivery in teaching, teaching aids, and others
Once defined, decide type, kind of data to be collected and experimental designs.
Research is preceded by preparation of comprehensive research proposal to guide the research process.
Research proposal is a step by step manual of all activities
It follows critical path of events achievable in sequences
Sound and effective research proposal is completed after thorough literature review of subject
When elements, variables and factors are known, plan and type of research are decided based on nature of problem or question to be answered
LITERATURE REVIEW ON SUBJECT OF
INVESTIGATION
Literature means published knowledge stored in any retrieval system, books, journals, periodicals, newsletters, microfilms, films, music, video and others.
Literature review means reading, extensively with a purpose of updating knowledge on specific subject and keep list of titles of published material
Bibliography; collection of titles of published papers and books on a subject indicating source of paper, no abstracts
Example all publications on mitochondria make bibliography of the mitochondria
Literature review is analysis of current state of knowledge
Helps research proposal to state clearly what will be known after research, that is not known now
Summarizes current state of knowledge, giving up-to-date bibliography
Purposes (aims) of reviewing literature
Determine relevant literature to study
Gain information on subject to current
level
Reveal investigations related to the
proposed research
Show how other researchers did on similar problems,
Obtain a method or technique of
dealing with the problem
Determine valid approach to the
problem,Reveal other
sources of data,Obtain new ideas and approaches
Form research in historical &
associational perspective and in relation to earlier
attempts in solving similar problem
Determine terminologies, parameters &
variables & their measurements
Be able to define general research questions within
current knowledge of literature
Be able to specify question in current
literature,Update knowledge to locate gap for
research
Places of literature storage Library of Universities and
Institutions; Computers on world wide web
(internet); National libraries and bibliographic
centers; National Bureau of Statistics; National Archives; Ministries; United Nations Organization offices
Two categories of sources of knowledge
Published Non published
Published Books Journals Periodical publications Annual subject reviews Proceedings of conferences,
symposia and professional society meetings
Non-published Ph.D. theses MSc. Dissertations Various reports Office documents Project reports Special collections and even
Minutes of meetings
Review summarizes literature precisely and succinctlyGathers specific knowledge to which the research will add
Reflect on review of related literature, what others have written in relation to what is planned
Review literature from a comprehensive perspective, like an inverted pyramid, broad end first
Constantly explain relatedness of proposed research to one in literatureDefine gap where new research will fill
Rewrite content of literature sources in own words and style, not copying texts of other authors
Read source, understand it, list points you remember, re-check points and join them into proper sentences
Outcomes of literature review;- Framed hypothesis within current
literature Defined scope of research and
objectives Defined type or category of
research within current literature Defined variables in the research
within current literature
Clear research plan Identified source of data (Research
sites) Identified action plan, logistics to
the research Ascertained objectives and
purpose of research Sequencing the activities Clear implementation strategy,
variables, parameters, factors
Conclude literature review by giving specific objectives of what the research is going to achieve
All the published materials must be indicated at two places in the researchers project plan / or text
In the text In the list of references
Citing literature in the text
Literature is cited in text to indicate source of scientific findings being quoted from retrievable sources
1. Normally journals 2. Sometimes books 3. May quote a review paper
References are required for
1. 0riginal findings 2. Knowledge established by previous researchers
3. Other information needing to indicate source
a) Articles with single author
(1) Subject comes before quotation
(i) Name of author placed at end of subject of the sentence
(ii) Author's name followed by coma (,), the year of publication
(iii) Author's name & year of publication placed in brackets (parentheses)
Examples Biotechnology is a primary tool of
control of foot and mouth diseases in cattle (Thompsen, 1995).
African goats produce more milk than Toggenburg breeds (Smith, 1989)
Lymphocytes cultured in vitro in RPMI 1640 medium proliferate within minutes (Belinger, 2000)
Total serum protein in African buffaloes lies within 100 ‑140 g/l limits (Mbassa, 1990)
(2) Author before subject (i) Name of author placed at
beginning or middle of subject of sentence
(ii) Author's name not followed by comma but year of publication
(iii) Only year is in parentheses
Examples Thompsen (2006) observed
that the native dense core protein of Babesia bovis has several epitopes, thus it is a candidate vaccine
Smith (2007) concludes that African goat milk contains an antiallergic factor
According to Belinger (2006) lymphocytes cultured in vitro in RPMI1640 medium proliferate within minutes
Mbassa (2006) observed the total serum protein in African buffaloes to be 100‑140 g/l
b) Articles with two authors
(i) Names of authors in order they appear in article placed at end of subject
(ii) Name of last author followed by coma (,) and year
(iii) Names of authors and year in parentheses
ExamplesThe butter‑fat content of zebu
cattle milk is 5.2 % (Mtenga and Aboud, 2004)
Endemic stability in East Coast fever is well established in Lake Victoria Region (Mpangala and Mollel, 2005)
Pars intermedia is not found in the pituitary gland of the greater flamingo, Phoenicopterus rubber rouseus (Mhowa and Dominico, 2007)
Authors before subject of sentence
(i) Names of authors as in article placed before subject
(ii) Name of last author not followed by coma, but year of publication
(iii) Only year of publication in parentheses
Examples Mtenga and Aboud (2004) found the
butter-fat content of zebu cattle milk to be 5.2 %
Mpangala and Mollel (2005) noted that endemic stability in East Coast fever is well established in Lake Victoria Region
Mhowa and Dominico (2007) did not find any pars intermedia in the pituitary gland of the greater flamingo, Phoenicopterus rubber rouseus
(c) Articles with more than two authors
Mention first author, followed by words
"et al.,” in italics
(i) Subject before authors Name of 1st author followed by
"et al.,“ placed after subject "et al ” in italics, then full stop
(.), coma (,) and year Name of author, "et al.,” and
year all placed in parentheses
Examples Biotechnlogy is the primary tool in the
control of foot and mouth diseases in cattle (Thompsen et al., 2006)
African goats produce more milk than Toggenburg breeds (Smith et al., 2007)
Lymphocytes cultured in vitro in RPMI 1640 medium proliferate within minutes (Belinger et al., 2006)
Total serum proteins in African buffaloes lie within 100 ‑140 g/l limit (Mbassa et al., 2006)
(ii) Subject after authors
Name of 1st author followed by "et al.” in italics placed after subject
"et al.“ not followed by coma (,) but year
Only year in parentheses
Examples1. Thompsen et al. (2005) observed that
foot and mouth diseases is more severe in cattle than in goats
2. Smith et al. (2003) concludes that African goats produce more butterfat in milk than Toggenburg
3. According to Belinger et al. (2006) lymphocytes cultured in vitro, in RPMI 1640 medium proliferate within minutes
4. Mbassa et al. (2007) observed the total serum protein in African buffaloes to lie within 100 ‑140 g/l limits
Compiling list of references
Gives detail of 1. Names of all authors
quoted 2. Only relevant references 3. References not cited, not to
appear 4. Names and, initials of all
authors said as "et al " in the text must appear in reference list
Information for each author quoted
(i) Surname followed by coma (,)
(ii) Initials (eg. M. M.) (iii) Year article published (e.g.
2006) (iv) Full stop (.) (v) Title of article (e.g. " Peripolar
cells form the majority of granulated cells in kidneys of antelopes and goats”
full stop.
(v) Full title (or std abbreviation of journal where article was published (e.g. Veterinary Parasitology" or Vet. Parasitol., Acta Anatomica or Acta Anat., Internat. J. Biotechnol.)
(vi) Volume of journal, colon (e.g. 51:) (vii) First page of article, dash
(e.g.3894- (viii) Last page of article (e.g. 3902) (ix) full stop.
Unless specially required, issue numbers in same volume of journal are not shown
Articles published in journals
Single author articles example
DeVos, A. J. 1978. Immunogenicity and pathogenicity of Babesia bovis in Bos indicus cattle. Onderstepoort J. Vet. Res. 45:119-123.
Two authors articles, Example
Fu‑Chu, He and Ghu‑tse Wu 1993. Molecular evolution of Cytokines and receptors. Exp. Hematol. 21:521‑524.
Articles with more than two authors
All authors, with initials must be provided
Word “et al ” not allowed to appear in list of references
ExamplesBrown, W. C., V. Shkap, Damine Zhu, T. C. McGuire, W. T, T. F. McElwain and G. H. Palmer 1998. CD4+ T lymphocyte and immuno-globulin 2 responses in calves immunized with Anaplasma marginale outer membranes and protected against homologous challenge. Infect. Immun. 66:5406-5413
Luziga, C., Yamamoto Y., Horii Y., Mbassa G. and Mamba K. 2006. Phagocytotic removal of apoptotic endocrine cells by folliculostellate cells and its functional implications in clusterin accumulation in pituitary colloids in helmeted guinea fowl (Numida meleagris). Acta Histochemica 108:69-80.
Quoting in a book with single author
(i) Name of author with initials, year of publication
(ii) Title of book (iii) Pages (e.g. 601‑623). (iv) Publisher (e.g. Lea and
Febiger) (v) Place of publication (e.g. Dar es
Salaam, Philadelphia, Toronto)
Example Klaus, G. G. B. 1987.
Lymphocytes: A practical approach. 261. IRL Press, Oxford England
Quoting from book of two authors
Example Losos, G. J. and Brown J. M. 1996.
Infectious tropical diseases of domestic animals. Langman Scientific and Technical. 742-795, Washington D.C.
Quoting from a book with many contributors
Example Keller, G. , K. Roger Tsang and I.
Kakoma. 1988. Advances in the in vitro cultivation of Babesia species. In: Babesiosis of domestic animals and man. Miodrag Ristic Eds, CRC Press Inc. Boca Raton Florida, 71-79.
Quoting chapter in a book
(i) Name(s) of author(s) with initials, year of publication
(ii) Chapter title(iii) Followed by In: title of book
followed by Name(s) of author(s) of book with initials, Pages, Publisher, Place of publication.
Examples Habour, C. and A. Fletcher 1991.
Hybridomas: Production and selection. In: Mammalian Cell Biotechnology: a practical approach, by M. Buttler, Oxford University Press, Oxford, 109‑138
(ii) Mathew, P., S. Michael and Juma K. 2002. Modeling of changes in belief. In: The psycho‑technological development for inductive changes in pastoralism. First Edition, by Williams P. K and Manus S. P. 2002. Oxford University Press, Nairobi Kenya. pp 234‑313
(iii) Hames, B. D. 1996. One dimensional polyacrilamide gel electrophoresis. In: Gel electrophoresis of proteins, Edited by B. D. Holmes and D. Rickwood. IRS Press Oxford. 1‑147.
Quoting from book with collection of articles (Multi-
authored book)
(i) Name of author(s) of article in the book
(ii) Title of article, year of publication
(iii) Followed by In: (iv) Name(s) of book authors (s)
with initials, then in parentheses (Editors)
(v) Title of book (vi) Publisher's name(s) (vii) Place of publication (viii) Pages read (first ‑ last)
OR (i) Name of author(s) of article in
book (ii) title of article (ii) Year published (iii) Followed by In: (iv) Title of book, followed by
Edited by then Name(s) of Editor(s) with initials, and pages read first ‑ last
Brown, L. R. and C. Flavin 1999. A new economy for a new century. In: L. R. Brown, C. Flavin and H. French (Editors), State of the World; A World-watch Institute Report on Progress Towards a Sustainable Society, 3‑21. W.W. Norton and Company, New York
Alternative quote from a book with many articles (multi-authored book)
(i) Name of author(s) of article in the book
(ii) year (iii) title of paper, followed by, In..
title of book, then Edited by, name(s) of author(s) with initials, publisher, place of publication and pages being referred to first – last
Example Brown, L. R. and C. Flavin 1999. A
new economy for a new century. In: State of the World; A World-watch Institute Report on Progress Towards a Sustainable Society, Edited by L. R. Brown, C. Flavin and H. French, 3‑21. W.W. Norton and Company, New York
Quoting from conference proceedings, symposia
(i) Name(s) of author(s) with initials
(ii) Year (iii) Title of article (iv) Followed by words;
Proceedings of (names of conference) held on (date & year) in (city and country)
(v) Name of Editor(s) with initials
(vi) Publishers (vii) Place of publication (viii) Volume of the proceeding (ix) Pages being read.
Examples Mbassa G. K., Young S., Kauzeni P. and
Nkangaga J. J. 2003. Quantitative impact analysis of wildlife conservation strategies: a study of impact of community-based conservation in Gonabis wildlife management area and rare antelopes in Game Reserves. Proceedings of the Fourth Scientific conference of the Tanzania Wildlife Research Institute Held at Impala Hotel, Arusha Tanzania Dec 3 to 6, 2003: 50-65;
Quoting from non regular publications
Workshops, Meetings, Other sources
(i) Name(s) of author(s) if possible (ii) Year of publication (iii) Publisher or custodian of work
Examples Mgongo F.O.K., Mellau, L.S.B., Mbassa,
G.K., Silayo R.S., Kimbita, E.N., Hayghaimo, A.A., Mlangwa J.E.D., Mbiha E.R., and Gwakisa P.S. 2007. Improving cattle selection and reproduction in the traditional pastoral sector with efficient control of tick borne diseases. Published by PANTIL-SUA Project Programme for Agricultural and Natural Resources Transformation for Improved Livelihoods. 27 pp
RESEARCH/ EXPERIMENTAL DESIGNS
An experiment is planned inquiry to get new facts, confirm hypotheses,
A trial to test validity of prior set hypothesis
Factors in selecting experimental designs
1. Solve problem or answer an un-resolved question
2. Contribute new information, improve understanding of matter
3. Develop/Test technology 4. Searching the unknown
In basic research new scientific information is generated
E.g. genome nucleotide sequence of animal/plant, gene e.g. fibronectin in liver cell, gene for resistance against disease in cattle, beans etc
Applied research generates, tests and validates technology
Experimental design selected to be optimal to answer research problem
New material
Research type
Availability of adequate
samples
Number of factors being
analysed
Statistical methods
Research types
Innovative, survey, analytical, experimentalProspecti
ve, retrospec
tive
Living or non living matter, social, species
Quantitative, qualitative
In vivo or in vitro macro or microscopic
Categories of experiments
Preliminary
•Large number of treatments to gather information, with or without replication
Critical•Compare responses of variable to different treatments, adequate units to detect differences
Innovative
•Generate new products, test them for their uses, compare with those in use
Planning the experiment
State research problem State problem in hypothesis form
List objectives
Select experimental design
Describe materials and methods in detail, including statistical analysis
Filter results from inputs
Filter results from inputs
Collect results
3. Perform experiment
2. State objectives
and methods
1. State problem & hypothesis
Qualities of well- designed experiments
Equality in
sample sizes and
member properti
es to eliminat
e systematic errors
Uniform treatme
ntAppropri
ate statistical design
Clear variables Proper plan for interpretation of results
Simplicity in
designProvide
necessary data
Experimental design to envisage
Proper results interpretation,Estimate errorError control
Biological materials vary greatly, due to inherent variability
(individual, intrinsic) and lack of uniformity in dispensing treatments
in the experiment
Strategies to control errors
Replication in space (location) Increase sample sizes Replication in time Randomization Inclusion of controls Blocking certain natural variations Refinement of methods and
chemicals Minimize mechanical errors
Control of errors
Replication in space (location), same time
Replication in experimental units at the same time
Replication in time, repeat experiment several times
Randomization, select samples by random principles
Inclusion of controls, omitting certain factors so that only a single factor is allowed to act on selected groups of treatments
Blocking certain natural variations, all samples uniform in size, age, material, time of treatment and other factors
Refine methods and chemicals, use very refined materials to eliminate blocking of active ingredients by impurities
HH
H
H
H O
O
OH
Minimize mechanical errors, investigator and experiment dispenser to be perfect
Common experimental designs
1. Single factor experimental design
Single factor varies, others constant Treatments; different levels of same
factor, e.g. testing suitable dosage for a growth factor, therapeutic drug, animal feed, mitogenic factor, mutagenic factor
Drug dosage studies in guinea pigs
Results Results Results Results
Report
Report
Publish
Patent
Sell technol
ogy
Disseminate
technology
2. Experimental design for two or more factors
These are factorial experiments
Effects of many different factors are
investigated simultaneously, economic & time
serving
A factor is a treatment, consisting of all
possible combinations
Magnitudes of effects are measured to determine contribution of each factor to effect
Factorial design reduces costs in animals, chemicals, feed, space,
time, labour, drug.
Example two or more milk replacers differing in composition investigated
in same group of animals at the same time in the same experiment
(a). Completely randomized
Animal 1 Animal 4
Animal 7Animal 8
Animal 5 Animal 2
Animal 3Animal 6
Animal 9
Uniform animals
(homogenous
Uniform treatments
Random selection at
all levels
Statistical methods T test, X square, GLM,
ANOVA
(b) Completely randomized block designanimals in blocks can be replaced by farm plots, cells, etc
Animal 19
Animal 22
Animal 25
Animal 26
Animal 23
Animal 20
Animal 21
Animal 24
Animal 27
Animal 1
Animal 4
Animal 7
Animal 8
Animal 5
Animal 2
Animal 3
Animal 6
Animal 9
Animal 10
Animal 13
Animal 16
Animal 17
Animal 14
Animal 11
Animal 12
Animal 15
Animal 18
Animal 28
Animal 31
Animal 34
Animal 35
Animal 32
Animal 29
Animal 30
Animal 33
Animal 36
Uniform study subjects
(homogenous)
Uniform treatments
Random selection at all levels
Measure effect of more than one
factor in an experiment
Economic and time serving
Variables include metabolic product,
growth level, disease occurrence,
immunity level
Animals grouped as natural as possible & as homogenous as possible
to eliminate errors (homogenous units called blocks or herds, pens etc)
Then apply randomised
treatments for the blocks
Statistical methods are
ANOVA, GLM, T test, X square
Analysis is performed by model; Yijk = +Ai + Bj + (AB)ij + eijk
Where Yijk = observation on kth animal receiving ith level of first factor A, = general mean common to all observations, Ai = an effect of ith level factor, first factor, Bj = an effect of jth level of second factor B, (AB)ij = an interaction effect due to combination between ith level of factor A and jth level of factor B and eijk = a random effect specific to each animal
Report
Publish Patent
Sell technolog
y
Disseminate
technology
3. Latin square experimental design when:-
Number of units very few
Limited facilities
Time consuming measurements
Same sample sizes of units at different
periods in sequence
One/more animal per treatmentStatistics; sum of squares
Too few samples reduces power of experiment
Carry over effects on using same animals causes confounding
4. Quasi-experiments
Design has some but not all of characteristics of true experiment
No random assignment of subjects to control and experimental conditions
Natural experiments, nature assigns subjects to conditions, e.g. trends in rainfall, wind, hurricane, crimes etc.
Characteristics
Matching instead of randomization, similar locations, not control but as comparison, also called nonequivalent group design
Time series analysis (longitudinal study over time), impact analysis
•not humans, contextual concepts; sociology, quality of life, anomalies, organization, disorganization, morale, climate, atmosphere
Unit of analysis
•Sufficient number of events to control threats to validity and reliability, independent variable is time
Validity
•Quasi-experiments are creative on causes of events, no control of independent variable•Baseline and natural interventions (legislation, relocation)
Control
In quasi-experiments use trend, not cause, major ones are syndromes or cycles, minor ones are normal or abnormal events
Quasi-experiment research designs to involve many different, but interrelated variables, causal relationships can be modeled to identify spurious (false), intervening and suppressing variables
Statistical methods in data analysis
Statistics is an applied mathematical science that provides an objective basis for analysing problems where the cause and effect of observations are not apparent
There are many statistical methods in biological research
For living organisms it is biometry Social sciences (non-parametric
statistics) Living things include their cellular parts,
organelles, molecules in cells (amino acids, vitamins, proteins, minerals, carbohydrates, lipids, water, nucleic acids and their combinations).
Statistical methods require 1. Quantitative measurements of
causal and effect factors, called variables (variates)
2. Populations 3. Samples 4. Measures of central tendency 5. Measures of dispersion and
hypotheses testing
Variables include plant number, height, pods, weight, milk quantity, protein content, number of genes, number of genetic recombinations on chromosomes, many others
Qualitative and quantitative measurements constitute data
Qualitative variables are non-numeric for example colour, taste, smell, molecular reaction in a cell, stain uptake and others
Data may be continuous, taking any value on a continuous scale (for example weight, height, milk yield)
Or discontinuous (discrete) appearing only as integral values eg, animal (cannot have half animal).
Populations A population (a universe)
means all individuals of a particular experimental set; animals, plants, cells, molecules and others) and includes all possible values of measurements, ranges from small to infinite
Samples A sample is a selection from
where observations, information or variables are recorded
The results of measurements in a sample can be extrapolated to the population
To be truly representative of the population, it must have been obtained by random selection or by purposive sampling
In a random sample each member of the population has equal and independent chances of being included.
The sample constitutes an experimental unit
Treatment in experiment is a procedure applied on a member of the sample, e.g. drug, milk, growth factor
Measures of central tendency (location)
Average (arithmetic mean) Statistical mean (central location
or ordinary value) Median (central number when
observations are arranged in ascending or descending order)
Mode (most frequently occurring observation value)
Measures of dispersion (Variability), first, second, third and fourth moments about the mean
First moment gives mean deviation about the mean
Second moment about the mean gives variance and standard deviation its square root
Third & fourth moments about the mean measure degree of skewness and curtosis of frequency distribution about the mean
Range is the difference between the highest and lowest values
Coefficient of variation (CV) or coefficient of variability (CV) measures precision of measurements
CV (%) = (std x 100) divided by the mean
Small CV indicates greater precision than in large CV, thus results are more reliable
Large CV indicate increased experimental errors
Hypothesis testing is based on statistical decision
A hypothesis is logical assumption about characteristics of a population.
The researcher guesses about the results of experiment, the guess is the hypothesis, which is tested
Examples a new gene may treat a disease a new drug is more effective than
standard drug a formulated material treats this
disease a new compound causes higher
plant growth or animal growth
a new breed of plant or animal yields more product
a new breed of animal is resistant to disease
a gene knockout results in abolition of disease, fertility, transcription, mutation, and many other examples of hypothesis
Statistical tests include T-test; 2 means tested, two way test Analysis of variance (ANOVA), where
more than two means are being tested, sample sizes in groups uniform
General linear models (GLM), more than two means tested, samples sizes in groups different
Chi (X) square, tests if one method is better than the other one or two way
Many others (parametric, non-parametric)
ANOVA partitions total variation into different sources, for example
among experimental units treated differently
among experimental units treated similarly
due to non-experimental variables
various interactions
ANOVA works if the sample sizes in different treatments are equal.
If the sample sizes are different another similar but stringent test similar to T –test is used, called General linear models (GLM)
Other statistical analytical methods
Regression Correlation Wilcoxon Shapiro-Wilk statistic Duncan’s multiple range
RESEARCH PROPOSAL
A research proposal is an idea intended or advised on a researchable subject, put
forward or suggested systematically and complete
Research Pro posals are suggestions, intentions, plans, schemes or requests
Systematic planned design of protocol to conduct research in specific subject
Objective •Provides objectivity & critical insight of planned research
Manual •Advanced manual to be followed in research process
Search •Defines information being sort
What • Sets out problem to be researched• What information being sort
Why •Why this is important to be known
How •How it is to be extracted from population
Where and when •When and where will the research be done
Addresses subject from
known towards
unknown, superficial to
detailed, general to
specificStates
objectives to achieve
Relates to collateral or related studies of
other researcher
s
Proposes data
necessary for solving problem
indicated, how to
collect data, treat,
process interpret
Determines needs to success
Indicates how results
be presented,
used, published
and disseminated
;Gives total budget for completion of research
Research is to provide
new knowledge & technology
Good research requires
early thinking on subject &
collection of adequate
recent literature
Research proposal is divided into three or more
components
Components of Research Proposal
TitleResearcher
sSummary
or Concept
Introduction
(background
information, problem
statement, justification
)Goal (aim)
Purpose and objectives,Hypothesis,
conceptual or theoretical frameworkLiterature review of subject
Research methodolo
gy materials methods
data analysis
work plansExpected outputs
BudgetResults
dissemination
Literature cited
(References)
Logic framewor
k of goals,
purpose, outputs,
objectives &
activitiesAppendic
es
Title
ShortClear
Reflects content of research
Describes content of research
Leads to understand concepts,
methods & output of proposed research
Researchers
Researchers involved
named
Their qualifications and addresses mentioned
Their affiliations and telephone numbers
given
Summary/concept
No more than 250 words on;
What is to be done
Why should it be done (problem)
How it will be done (what samples)
Where and when research is to be done
Who are the beneficiaries
Selecting topic or subject of research
Identify interests or puzzles
Identify puzzling points; scientific, social,
economic, health, political, cultural
Identify keywords on topic
Express puzzle in specific keywordsDefine topic by
analyzing keywords
Formulate topic by searching for articles to
identify researchable problem
Qualities of a good research topic
Researchable, instruments are easily formulated, population,
samples, objectives, measurable
Contributes new knowledge
Findings publishable
Provocative, open to varied views and interpretations;
Clear and focused, not vague or ambiguous
Too wide subject, no limit of scope
Vague topic not possible for in-
depth studyToo complex study
subject
Poor timingLimited accessibility
to materials
Problems in topic selection
Selection of Title
Title is heading, label
or tag, describes
study, mini abstract, portrays
summary of key idea(s)
Discusses topical issues in science, business, life and living or others,
analyzing factors enhancing or hindering success of generations
Formulated after
identification of research
topic (subject)
Identify title keywordsReflect on key issues
Identify independent and dependent variables, link them in title
Evaluate title; clear, specific, independent & dependent variables identified
Control length to 12-15 words only
Steps in title selection
Qualities of good and effective research proposal
title
Brief and specificSalient & have strong impact
Easier to see independent &
dependent variables
In line with objectives
FocusedSummary of what
study is about
Portray aims of study
Reflect relationship between
independent & dependent variables
Show researchable subject with measurable
results
Unambiguous, not to cause
various interpretations
of the study
Challenges in title selection
Not specific, varied
interpretations
Too long and too wordy
Difficult to understand
Lack of consistency, objectives not apparent
or different from problem statement &
or methodology
IntroductionOpens study
Discusses background to research
States & defines problemAims & objectives stated & how
work will progress givenEstablishes existence of problem &
need/justifies investigation
Sub-divisions of introduction
.
Background knowledge
Statement of problem
Justification
Aims & objectives, hypothesis,
research questions
Significance of study,
limitations, conceptual &
theoretic frameworks
Background information
Knowledge on the problem in
context of current
literature & status
Gives setting of study
Determines user/client or beneficiaryExplains the
matter, what is known about the subject
Scientific, establish a cause and
effect relationshipPrerequisite knowledge
before problem is stated
Background information reveals
What has brought about need for study;
Challenges faced due to the issue
Problem or opportunity exists & needs being
addressed;Opportunities for
improvement
Current view of research problem;
Familiarity with subject with clear linkage of flow of knowledge
Qualities of good background information
Brief, specific, summary literature review;
Generates concerns on problem &
opportunity of solution
Gives a glimpse of the
research problem;
Gives idea on how the
proposal is structured
Uses simple, straightforwar
d fluent language;
Informative, persuasive,
states urgency of addressing problem so
resources be allocated
Challenges in writing background information (BI)
Confusion between BI & literature review
(LR). LR studies related areas, BI is short,
briefly on why to study & opportunities after
Confusion of BI with justification of study, but BI is to give brief overview of problem
Bad quotations
Lack of clarity, jargon, slang, trendy words,
abbreviations, colloquial, redundant phrases, confusing
language
Qualities of good backgrounds
Brief, specific overview of problem
Simple straightforward language
Previous studies justifying study are
cited
Researchers show familiarity with current events and information
on problem
Statement of problem
Identified problem is reason for research
Problem identification may involve
exploratory research (diagnostic survey) to
collect background data
One way; use logical
approach to establish causation
Problems have logical
sub‑components
(sub‑problems)
When solved
separately, sub‑problems resolve
main problem
Sub‑problems are researchable units, making interpretation of
data more apparent, adding up to totality of problem
Research problem must be specific to make methods specific & appropriate, set precise limits
of problem area
Qualities of sound & good statement of problem
Clear & conciseHas an impact on the whole topic
Indicates urgency of research & that
research is definitely needed
Problem researchable
through collection and analysis of
data
Steps to write research problem
Reflection—start with idea, what
kind of question is to be answered
Present research ideas or puzzles,
then assess selected topic &
title
Reflect topic, independent &
dependent variables of investigation
Identify uncertaintie
sState why is a problem,
how will communities be better
off after research
Formulation; when
problem is identified
formulate it clearly. Indicate
how it came out,
personal observation or previous
research
Justification - explain
repercussions to follow if problem is not addressed, use statement to show that research has to be performed
Challenges in research problem formulation
Clarity• Research problem is lack of
clarity
Unity
• lacks unity and relationship to objectives, independent and dependent variables & literature review
Urgency
• Lacks urgency, no urgency for investigation, no evidence that if not addressed, repercussions are serious for country, people, lost opportunities
Emotion
• Statements lack objectivity, reinforce emotions over topic, problem not easily investigated by collection and analysis of data.
Goals (Aims) & Objectives
Objectives are aims the research envisages to achieve, eventually the purpose of study
In any research there are indicators of
intention & direction of study
Goals or aims are intentions, goals or
what research strives to achieve, long term
objectives e.g. national development
Aim is general statement
• Reflects intention or purpose of research, stated in terms not easily measurable
Aims assist in formulation of objectives
• Pinpoints purpose of study, reveals whether research is urgent or not
Quality aims & goals of research proposal
Pragmatic;State purpose of
study, not referring to specific
achievements
State accomplishment of
group not individuals
Stated in general terms providing
direction for research
development
Broad enough to lead to specific
objectivesClearly stated & are
reflective
Formulation of study aims, goals & purpose
Reflection – think, decide on what to
accomplish by end of the study, analyze title
Formulation – write purpose of study (what
to accomplish within time)
Analysis – analyze aims to confirm they
address research problem & questions
Challenges in formulation of aims
Lack of clarity, purpose of study is
not articulated
Lack of cohesion - no clear link
between title, purpose, objectives
or problem
Over-ambitious aims – not
achievable by resources & time
available
Objectives
Objectives; intentions or purposes stated in specific measurable terms
Results are evaluated via objectives Specific objectives constitute means
by which aim/ goal is achieved Specify what to be done Are operational, stating specific
tasks with measurable results to be carried out
Objectives are vital because they guide
Methods, instruments, study area;
Data collected, analysis & report
Literature review;Precision on what to
accomplish;Study into defined
parts
Variables, Evaluation;Break aim into achievable &
measurable pieces, Consistent focus
activities in sequence
Assumptions, limitations
Factors facilitate completion of research
Factors preventing research to be done
Practical & theoretical limitations make results valid and applicable or
inapplicable
Qualities of suitable objectives
SpecificMeasurable
Focused
Cover problem coherently &
logically
SystematicOperational
Realistic
Methodology (materials & methods)
Describes systematically in detail materials, tools, experimental
designs, methods, logistics how to research to cover objectives
Methodology is to be clear
on experimental
procedure on…
What data required;What standards data to meet;How to collect,
process, analyze data
Selected materials for testing;Study area;
Procedures for measuring variables;Units of
measurements
Explain how data will be analyzed;
Trimmed, spliced to show trends or associations;
Communication, dissemination &
utilization of results
Work plans
Expected
outputs
Logical framework
matrix
Monitoring & evaluation
Matrix of research work plan
Goal(Aim)
1
1
Objective/Output
1
2
Activities
123
123
Outcome to observe
123
123
Month to observed outcome
Jun 2009Dec 2009Mar
2010Jun 2010Sep
2010Dec 2010
Logic framework matrix or logframe
Effective tool for
planning and
evaluation of research
States conditions necessary for research
to succeedSummarized in four
column table
Rows represent different levels of
project goal, objectives, outputs,
activities
Columns indicate how the achieved objectives are measured, and assumptions for achieving results
In vertical logic are narrative summary at goal, purpose,
objectives or outputs, and inputs in terms of activities levels
Inputs include personnel,
physical and financial resources
Outputs are measures of what comes after inputs
energy (inputs & activities cause outcomes [Inputs
are similar to independent variables])
outputs are outcomes caused by activities
[outputs are similar to dependent variables]
The goal is
ultimate objective,
not the immediat
e objective
The immediat
e objective
is the purpose, which is the main output
Goal and purpose
•Relationship between goal and purpose is less direct and causal
Factors •Many exogenous factors influence the goal
Contribution to goal
•Achieving the purpose is necessary but not sufficient to achieve goal
In horizontal logic are
verifiable indicators & means of verification
evidence of achievement of
research project (in deed every project),
& how that evidence is found & measured
Indicators & their means of
verification show criteria for attaining
objectives of nature, quantity, quality and time
Assumptions are factors which are not controlled by the research but
influence it (external factors)
Narrative summary
Goal
Purpose
Objectively verifiable indicators
Measure of goal
End of project status
Means of verification
Sources of informatio
nMethods
used
Sources of information
Methods used
Assumptions (Risks)
Assumptions affecting Purpose,
Goal
Assumptions affecting the Output,
Purpose linkage
Logic framework
Narrative summary
Inputs
Output 1
Activities
Objectively verifiable indicators
Nature & level of
resources, necessary
costs, planned
starting dateQuantitativ
e magnitude
s of outputs
and planned
datesQuantitative magnitudes of outputs
and planned dates
Means of verification
Sources of
information
Sources of informationMethods to
use
Sources of information, Methods to
use
Assumptions (Risks)
Initial assumptions
about the project
Assumptions affecting
Input/Output linkage
Assumptions affecting activities
Narrative summary
Inputs
Output 2
Activities
Objectively verifiable indicators
Nature & level of
resources, necessary
costs, planned
starting dateQuantitativ
e magnitude
s of outputs
and planned
datesQuantitative magnitudes of outputs
and planned dates
Means of verification
Sources of
information
Sources of informationMethods to
use
Sources of information, Methods to
use
Assumptions (Risks)
Initial assumptions
about the project
Assumptions affecting
Input/Output linkage
Assumptions affecting activities
Monitoring & evaluation plan
Goal(Aim)
1
1
Objective/Output
1
2
Activities
123
123
Output quantity
123
123
Month to observe output
Jun 2009Dec 2009Mar
2010Jun 2010Sep
2010Dec 2010
List all literature cited in the text
DATA COLLECTION
Samples may be whole mammals, birds, reptiles, amphibians, fish, higher or lower plants, heminthic worms; nematodes, cestodes and trematodes,
fungi, bacteria and virus or their parts, cells, tissues and organs, secretions such as milk, sweat, tears, hormones, antibodies, vitamins, proteins, urine, bile acid, bile, cell, embryo, saliva, semen or other
They may be macromolecules such as nucleic acids, proteins, fats, carbohydrates or product, metallic or non metallic, feeds, pathologic tissues, blood, plasma, serum, dyes or various compounds, simple and complex
DATA ANALYSIS AND PRESENTATION
Biological samples collected from plants or animals are processed in the laboratory and analyzed to yield results by standard biological methods
Samples may be whole mammals, birds, reptiles, amphibians, fish, higher or lower plants, heminthic worms; nematodes, cestodes and trematodes, fungi, bacteria and virus or their parts, cells, tissues and organs, secretions such as milk, sweat, tears, hormones, antibodies, vitamins, proteins, urine, bile acid, bile, cell, embryo, saliva, semen or other
They may be macromolecules such as nucleic acids, proteins, fats, carbohydrates or product, metallic or non metallic, feeds, pathologic tissues, blood, plasma, serum, dyes or various compounds, simple and complex
Each biological product is analyzed by a standard biological procedure in various instruments such as microscopes, celloscopes, spectrophotometers, scans, dyes, chromatograph, and many others
The results of field data, observations, morphology, measurements, questionnaires, innovation data; new material, drug, vaccine, machine, bacteria, feed to be found and tested are described
Impact data for a process going on, qualitative and quantitative data information obtained is also presented in detail.
Data is first entered in electronic form and storage (Data Base or Spreadsheet), then cleaned, statistical methods for analysis chosen and executed
Presentation of Results
Descriptive texts Tables Figures (graphs, pictures,
diagrams, charts, histograms, box plots or other drawings)
Describes in detail salient features of results obtained referring to tables and figures which explicitly or brightly show the characteristic or trend being reported
Tables classify data to facilitate comparisons and reveal relationships or trends of the data being presented
Tables are self-explanatory, giving more illustrative information in support of the description in the text.
There are many varieties of organizations of research results in tables, depending on the type of experiment, its design and treatment structure
Tables are numbered in the proper sequence relating to the flow of ideas and/or the sequence of events in the text but typed on separate pages
In the text, the first letter of the word table(s) is capitalized e.g. Table 1, Tables 4 and 5, even when it appears in the middle of the sentence
Tables may be arranged in different ways, but the format that demonstrates results most effectively is selected
Many types of data have decimal points. It is important to reduce decimal places to the precision required in the parameter being determined
Figures in form of pictures, diagrams, charts are commonly used in biological presentations, the most commonly used are line graphs, bar or pie charts in two or dimensions with X, Y or Z axes
Line graphs; suitable for presenting a relationship between continuous (quantitative) variables e.g milk or rice grain yield response to varying rates of fertilizer levels
Bar charts are used for discrete (discontinuous) data e.g. frequency distribution
Pie charts are used to present the relative magnitude of the components of a whole unit
In degree of accuracy in presenting data, graphs can be ranked in descending order of line graphs, bar and lastly pie charts
In bar charts, Y axis begins at the zero so that both the relative and absolute bar heights reflect accurately magnitude of treatment means and their differences, truncating it exaggerate the differences
A line graph cannot be valid unless it is based on a minimum number of three treatments i.e three data points)
Tables are more accurate in data presentation
DATA INTERPRETATION
1. Analysis of causation of effect by a factor
2. Interpretation in multi-causal factors and multi-effects
3. Statistical association 4. Application of statistics in
interpreting data
1. Analysis of causation of effect by a factorA Variable is a property, a factor, or a characteristic of a system, an individual, a group, or a system that changes or causes a change in quality or quantity of another property or characteristic of any system
variable Outcome Outcome
Putative causes of an outcome (or effect) such as transcription level, translation level, death of a cell, disease in an animal, milk produced, protein produced, etc
Putative causes are exposure or risk factors (as independent predictor or variable, or explanatory variables) producing the outcome of interest (the effect)
(e.g. disease, gene transcript, response, productivity, cell secretion, absorption, reproduction, .growth or others). An effect is a measurable response and is a dependent variable
An Outcome or Effect is a measurable outcome of action of a cause
A determinant is any factor that when altered produces a change in the frequency or magnitude or characteristics of a dependent variable
Factors are such as age, breed or sex in animals and many others in non animal biological or non biological systems
Association of causal and outcome variables may be true associations or spurious associations (chance, bias, confounding)
Many determinants are external to the system (animal, cell, culture, tissue others)
Internal factors relate to the intrinsic elements of the system (such as the pathogenesis of a disease in an animal).
Sufficient cause is amount of factor that reaches a threshold to cause an outcome
Purpose of data analysis in many experiments and research is to determine whether suspected factor (agent, independent variable) is the cause of a specific outcome or response (dependent variable).
Early (Henle-Koch) Guidelines for determining causal-effect relationship Independent variable casual
(agent) must be present in every case of that outcome (dependent variable);
Independent variable must not be present in other systems (where the outcome of interest is not there);
Independent variable must be isolated from dependent variable (tissues, cultures);
Independent variable must be capable of inducing the response or the outcome of interest under controlled experimental conditions
Henle-Koch guidelines are not sufficient because outcome of interest may not be caused by a single agent, i.e. dependent variable may be affected by many independent variables (multi-causal effects)
Causal agent (independent variable) may also cause many effects (many outcomes) or many dependent variables. There are;
(1) Multiple aetiological factors/ agents of effects (e.g. disease, genes, secretions, hormones) (multi-causality in addition to mono-causality for one causal factor)
(2) Multiple effects of single causes
(3) Causes may also be affected by other factors (quantitative causal factors or determinants)
2. Interpretation in multi-causal factors and multi-effects
Guidelines for multi-causal agents and multi-effect outcomes & many outcomes are analyzed by;
Methods of agreement Methods of difference Methods of concomitant
variation Methods of analogy Methods of residue
Method of agreement
An outcome (an effect) occurs under a variety of circumstances but there is a common factor. This factor is the cause of the outcome
Method of difference If circumstances where an effect
(dependent variable) occurs are similar to those circumstances where causal factor ( independent variable) does not occur, except where there is one factor difference, this factor or its absence is the cause of outcome (effect of interest).
Method of difference is basis for keeping all factors constant except for one factor in experimental research design
Methods of concomitant variation If independent factor (Causal factor)
and effect (outcome, response, dependent variable) have a dose dependant relationship factor may be cause of response
Independent factor (variable) or agent whose strength or frequency varies directly with occurrence of outcome convinces a causal- effect relationship
Method of analogy
If the distribution of an outcome is sufficiently similar to another factor, it may be that there is a causal-effect relationship
Method of residue
If the factor only explains X% of the outcome other factors must be identified to explain the remainder or (100-X%)
to increase the experimental precision
Control variables that are not of interest
Increase the sample size Repeat experiment in another
location Sampling without bias
Guidelines to concept on inferences in causation (developing causal inferences)
Incidence of outcome must be higher in the material animal, cell or other system which has been exposed to the putative causal agent than in non exposed systems
Exposure should be more common in cases where outcome occurs/has occurred than in those where outcome (effect) has not occurred
Exposure to putative cause must precede outcome
There should be a spectrum of measurable responses on dependent variable
Elimination of putative cause results in elimination of the outcome
Preventing or modifying the independent variable decreases or eliminates the outcome or effect
The outcome must be reproducible experimentally
The sequence for the researcher in assessing causation is;
to demonstrate that association exists
To assess likelihood that causal association exists
To elaborate nature of causal association
3. Statistical association
For factor to be causally associated with outcome, rate of outcome in exposed members must be different from not exposed members.
To evaluate probability that sampling error may have accounted for observations a statistical test is required
Member status
Outcome present
Outcome absent
Total
Exposed a b a+b
Not exposed
c d c+d
Total a+c a+b+c+d
A 2x2 table displaying the relationship between two dichotomous variables, one the factor, the other for the outcome
Proportional or rate of interest Exposed to factor in population p(F+) Outcome (effect) is positive in
population (E+) Affected and exposed to factor p(F+
and D+) Affected in exposed members
p(D+/F+) Affected in non exposed members
p(D+/F-) Exposed to factor in effected
members p(F+/D+) Exposed to factor in non affected
members p(F+/D-)
Working out rates from statistics (representing parameters)
F+ = (a + b)/n D+ = (a + c)/n F+ and D+ = (a/n) D+/F+ = a/(a + b) D+/F- = c/(c+d) F+/D+ = a/(a+c) F+/D- = b/(b+d) n=a+b+c+d
A statistical test would be the chi-square X2
X2 = [Іaxd)-(bxc)l- O.5n] x n (a+b) x (c+d) x
(a+c) x (b+d)
In a 2x2 table all statistics have one degree of freedom, the critical value for significance at 5% level is 3.84
4. Application of statistics in interpreting data
Statistical difference is a function of;
The magnitude of difference The variability of difference The sample size
Strength of association between causal factor and effect (outcome, response) is called Relative Risk, Risk ratio, incidence ratio, prevalence ratio.
Relative Risk is calculated as ratio between rate of response in members exposed to causal factor and rate of response in members not exposed to causal factor.
If there is no association between cause and outcome RR is 1.
The greater the departure of RR from 1 larger or smaller, the stronger the association
Attributable fraction (AF) is the proportion of response (outcome) in the factor exposed group that is due to the factor
Measures of association for independent proportions in 2x2 tables.
RR = [a/(a+b)]/[c/(c+d)]
Odds ratio = ad/bc
Effect of factor in exposed members here is some effect in factor negative group/members, not all outcome in factor exposed members is due the factor
In calculating attributable rate assumption is that other factors which lead to some outcome in factor negative group operate with same frequency and intensity in factor positive group
This absolute difference is called Attributable Rate (AR)
AR is calculated by subtracting the rate of response (outcome) in the un-exposed group from the rate in the exposed group
AR is the rate of outcome response in the group due to the exposure
The larger the AR the greater the effect of the factor in the exposed group
AR = [a/(a+b)]-[c/(c+d)] AF = AR/[a/(a+b)] or = (RR-I)/RR
Causal Inferences in Observational Studies
In interpreting ARs and AFs assumption is that there is a cause and effect relationship
However statistical associations do not in themselves represent a causal association
There are important precautions needed to eliminate this problem
Solutions for statistical deficiencies
Select sampling method better for measuring associations, such as cohorts
Refine independent and dependent variables by using cause specific rather than crude factors, this strengthens associations
Seek other variables that produce or explain associations or lack of associations (eliminate confounding)
Confounding variables A confounding variable is one
associated with the independent variable and the dependent variable
Confounding variables may be determinants of an outcome
For example a disease e.g. mastitis may be associated with age, castration is associated with age, therefore the effect of age is to be taken into account
Control of confounding variables
Exclusion (restricted sampling), select units with only one level of confounding or without confounding
Matching, Equalize the frequency of confounding variable in two groups being compared, e.g. cohorts
Analysis, stratify data and display in a series of 2x2 tables, one table for each level of confounding
A method that summarizes associations in multiple tables is called Mantel-Haenszel technique, here the Odds ratio (OR) is the measure of association.
OR = (Σad/n)/( Σbc/n) These methods are not mutually
exclusive, all may be applied at the same time
Criteria of judging causal inference
1. Time sequence, the factor to cause an outcome must precede the outcome, well designed cohort studies give best experiments
2. Strength of association, is measured RR or Odds ratio. The greater the . departure from 1, the strong the association
1. Dose-Response relationship, there exists an association if increasing amount of causal factor produces higher and higher level of outcome
2. Coherence, an association is more likely to be causal if it is sensible
3. Consistence, an association is likely to exist if it is supported by similar findings under different conditions
4. Specificity of association, a single causal factor (if crude) may produce a number of effects (outcomes). Refining the causal variable produces a better outcome thus strengthens the association
Elaborating Causal Mechanisms
If an association of two variables is causal the nature of the association may be determined by
Indirect and direct causes the causal association is direct when there is no intervening variable between the factor and the outcome.
Both the independent and the dependent variables are measured at same level of organization (individual, cell, tree, fruit, group of trees, farm). All other causes are indirect.
Necessary and sufficient causes; another dimension of classifying determinants. A necessary cause is one without which the outcome cannot occur. Sufficient cause is one that always produces the outcome
Path model causation; Path model provide other ways for conceptualizing, analyzing and demonstrating the causal effects of multiple factors. Variables are ordered and causal effects flow along arrows and paths
Statistical methods are applied to estimate the relative magnitude path coefficients of each path
Displaying effects of multiple factors; Produce ven diagram, important when the risk values increase steadily in number of putative causal factors. Calculate RR or OR of outcome for each combination of independent variables relative to the lower risks (group member outcome).